Image deblurring based on the estimation of PSF parameters and the post-processing

Lee, Jong Min; Lee, Jehee; Park, Ki Tae; Moon, Young Shik

doi:10.1016/j.ijleo.2012.06.067

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…In a linear motion blur case, in which the motion occurred in the direction of a straight line, the blur kernel can be formulated using the motion blur parameters, i.e., direction and length. The frequency spectrum based on the gradient variations has been used in [ 38 ] for motion blur parameter estimation. Authors in [ 39 ] explored a modified cepstrum domain approach for this purpose.…”

Section: Related Workmentioning

confidence: 99%

Using a Blur Metric to Estimate Linear Motion Blur Parameters

Javaran

Hassanpour

2021

Computational and Mathematical Methods in Medicine

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Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.

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Section: Related Workmentioning

confidence: 99%

Using a Blur Metric to Estimate Linear Motion Blur Parameters

Javaran

Hassanpour

2021

Computational and Mathematical Methods in Medicine

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“…Such methods have certain limitations because the results depend on the accuracy of the estimated blur kernel, and are also limited by the restoration ability of the chosen non-blind deblurring algorithms. Other blind deblurring algorithms use parameterized prior models to alternately iterate the two processes of blur kernel parameter estimation and latent image reconstruction until a good result is obtained 4,5 .…”

Section: Introductionmentioning

confidence: 99%

Blurred image restoration conditional generative adversarial network based on hybrid channel and spatial attention

Hu,

Yang,

Dong

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Imaging Systems

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In this paper, we propose a conditional generative adversarial network (CGAN) for restoring blurred image. The design of the generator derives from classic U-net network, but to improve its expression ability, we first modify the U-net by replacing some deep layers with stacked residual modules. Furthermore, we combine the channel and spatial attention modules and embed them into the generator to force it paying more attention to important channels and blurred local space. For loss function design, we comprehensively incorporate the pixel loss, perception loss and adversarial loss to enhance the performance of the proposed CGAN. Finally, the GoPro dataset is used for training and evaluating the effectiveness of the network. The results show that the proposed CGAN can achieve restored image of very high quality which is comparable with some state of the art methods.

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Deblurring Image Using Motion Sensor and SOM Neural Network

Lee

Zhuo

2018

Lecture Notes in Electrical Engineering

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Image deblurring based on the estimation of PSF parameters and the post-processing

Cited by 10 publications

References 8 publications

Using a Blur Metric to Estimate Linear Motion Blur Parameters

Using a Blur Metric to Estimate Linear Motion Blur Parameters

Blurred image restoration conditional generative adversarial network based on hybrid channel and spatial attention

Deblurring Image Using Motion Sensor and SOM Neural Network

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